Abstract：
BACKGROUND: Accurately estimating the 6D pose of snake-like wrist-type surgical instruments is challenging due to their complex kinematics and flexible design.
METHODS: We propose ERegPose, a comprehensive strategy for precise 6D pose estimation. The strategy consists of two components: ERegPoseNet, an original deep neural network model designed for explicit regression of the instrument\'s 6D pose, and an annotated in-house dataset of simulated surgical operations. To capture rotational features, we employ an Single Shot multibox Detector (SSD)-like detector to generate bounding boxes of the instrument tip.
RESULTS: ERegPoseNet achieves an error of 1.056 mm in 3D translation, 0.073 rad in 3D rotation, and an average distance (ADD) metric of 3.974 mm, indicating an overall spatial transformation error. The necessity of the SSD-like detector and L1 loss is validated through experiments.
CONCLUSIONS: ERegPose outperforms existing approaches, providing accurate 6D pose estimation for snake-like wrist-type surgical instruments. Its practical applications in various surgical tasks hold great promise.
METHODS: We propose ERegPose, a comprehensive strategy for precise 6D pose estimation. The strategy consists of two components: ERegPoseNet, an original deep neural network model designed for explicit regression of the instrument\'s 6D pose, and an annotated in-house dataset of simulated surgical operations. To capture rotational features, we employ an Single Shot multibox Detector (SSD)-like detector to generate bounding boxes of the instrument tip.
RESULTS: ERegPoseNet achieves an error of 1.056 mm in 3D translation, 0.073 rad in 3D rotation, and an average distance (ADD) metric of 3.974 mm, indicating an overall spatial transformation error. The necessity of the SSD-like detector and L1 loss is validated through experiments.
CONCLUSIONS: ERegPose outperforms existing approaches, providing accurate 6D pose estimation for snake-like wrist-type surgical instruments. Its practical applications in various surgical tasks hold great promise.
摘要：
背景：由于其复杂的运动学和灵活的设计，准确估计蛇形腕式手术器械的6D姿态具有挑战性。
方法：我们建议ERegPose，精确的6D姿态估计的综合策略。该策略包括两个组成部分：ERegPoseNet，为仪器的6D姿态的显式回归而设计的原始深度神经网络模型，和一个带注释的模拟外科手术的内部数据集。要捕获旋转特征，我们采用单镜头多盒检测器（SSD）-类检测器来生成仪器尖端的边界框。
结果：ERegPoseNet在3D平移中实现了1.056mm的误差，在3D旋转中0.073rad，平均距离（ADD）为3.974毫米，表示整体空间转换误差。通过实验验证了类似SSD检测器和L1损耗的必要性。
结论：ERegPose优于现有方法，为蛇形腕式手术器械提供准确的6D位姿估计。它在各种手术任务中的实际应用前景广阔。
方法：我们建议ERegPose，精确的6D姿态估计的综合策略。该策略包括两个组成部分：ERegPoseNet，为仪器的6D姿态的显式回归而设计的原始深度神经网络模型，和一个带注释的模拟外科手术的内部数据集。要捕获旋转特征，我们采用单镜头多盒检测器（SSD）-类检测器来生成仪器尖端的边界框。
结果：ERegPoseNet在3D平移中实现了1.056mm的误差，在3D旋转中0.073rad，平均距离（ADD）为3.974毫米，表示整体空间转换误差。通过实验验证了类似SSD检测器和L1损耗的必要性。
结论：ERegPose优于现有方法，为蛇形腕式手术器械提供准确的6D位姿估计。它在各种手术任务中的实际应用前景广阔。
